




"""simple docstring"""






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase__			=		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")






@dataclass
class lowerCAmelCase__      :






			'''simple docstring'''



			lowerCamelCase__ =     field(
			    default="""cifar10""",      metadata={"""help""": """Name of a dataset from the datasets package"""}		)
			lowerCamelCase__ =     field(
			    default=lowercase,      metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}		)
			lowerCamelCase__ =     field(
			    default=lowercase,      metadata={"""help""": """The column name of the images in the files."""}		)
			lowerCamelCase__ =     field(default=lowercase,      metadata={"""help""": """A folder containing the training data."""}		)
			lowerCamelCase__ =     field(default=lowercase,      metadata={"""help""": """A folder containing the validation data."""}		)
			lowerCamelCase__ =     field(
			    default=0.15,      metadata={"""help""": """Percent to split off of train for validation."""}		)
			lowerCamelCase__ =     field(
			    default=lowercase,      metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of training examples to this """
			            """value if set."""
			        )
			    },      )
			lowerCamelCase__ =     field(
			    default=lowercase,      metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
			            """value if set."""
			        )
			    },      )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       {}
							if self.train_dir is not None:
											_lowerCamelCase	:     int       =       self.train_dir
							if self.validation_dir is not None:
											_lowerCamelCase	:     Dict       =       self.validation_dir
							_lowerCamelCase	:     List[str]       =       data_files if data_files else None






@dataclass
class lowerCAmelCase__      :






			'''simple docstring'''



			lowerCamelCase__ =     field(
			    default=lowercase,      metadata={
			        """help""": (
			            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
			        )
			    },      )
			lowerCamelCase__ =     field(
			    default=lowercase,      metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}		)
			lowerCamelCase__ =     field(
			    default=lowercase,      metadata={
			        """help""": (
			            """Override some existing default config settings when a model is trained from scratch. Example: """
			            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
			        )
			    },      )
			lowerCamelCase__ =     field(
			    default=lowercase,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}		)
			lowerCamelCase__ =     field(
			    default="""main""",      metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""},      )
			lowerCamelCase__ =     field(default=lowercase,      metadata={"""help""": """Name or path of preprocessor config."""}		)
			lowerCamelCase__ =     field(
			    default=lowercase,      metadata={
			        """help""": (
			            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
			            """with private models)."""
			        )
			    },      )
			lowerCamelCase__ =     field(
			    default=0.75,      metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}		)
			lowerCamelCase__ =     field(
			    default=lowercase,      metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}		)






@dataclass
class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     field(
			    default=1e-3,      metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}		)







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     Tuple       =       torch.stack([example['pixel_values'] for example in examples]						)
				return {"pixel_values": pixel_values}







def 	_snake_case     (							):
				_lowerCamelCase	:     int       =       HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)						)
				if len(sys.argv						) == 2 and sys.argv[1].endswith('.json'						):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								_lowerCamelCase	:     Any       =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
				else:
								_lowerCamelCase	:     List[Any]       =       parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('run_mae'				,       a__				,       a__						)

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'				,       datefmt='%m/%d/%Y %H:%M:%S'				,       handlers=[logging.StreamHandler(sys.stdout						)]				,       )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				_lowerCamelCase	:     Optional[Any]       =       training_args.get_process_log_level()
				logger.setLevel(a__						)
				transformers.utils.logging.set_verbosity(a__						)
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + f'''distributed training: {bool(training_args.local_rank != -1						)}, 16-bits training: {training_args.fpaa}'''						)
				logger.info(f'''Training/evaluation parameters {training_args}'''						)

				# Detecting last checkpoint.
				_lowerCamelCase	:     List[Any]       =       None
				if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
								_lowerCamelCase	:     str       =       get_last_checkpoint(training_args.output_dir						)
								if last_checkpoint is None and len(os.listdir(training_args.output_dir						)						) > 0:
												raise ValueError(
												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    'Use --overwrite_output_dir to overcome.'						)
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'						)

    # Initialize our dataset.
				_lowerCamelCase	:     Optional[int]       =       load_dataset(
				    data_args.dataset_name				,       data_args.dataset_config_name				,       data_files=data_args.data_files				,       cache_dir=model_args.cache_dir				,       use_auth_token=True if model_args.use_auth_token else None				,       )

				# If we don't have a validation split, split off a percentage of train as validation.
				_lowerCamelCase	:     Any       =       None if 'validation' in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split				,       a__						) and data_args.train_val_split > 0.0:
								_lowerCamelCase	:     Any       =       ds['train'].train_test_split(data_args.train_val_split						)
								_lowerCamelCase	:     Optional[Any]       =       split['train']
								_lowerCamelCase	:     Dict       =       split['test']

				# Load pretrained model and image processor
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				_lowerCamelCase	:     Union[str, Any]       =       {
				    'cache_dir': model_args.cache_dir,
				    'revision': model_args.model_revision,
				    'use_auth_token': True if model_args.use_auth_token else None,
				}
				if model_args.config_name:
								_lowerCamelCase	:     Dict       =       ViTMAEConfig.from_pretrained(model_args.config_name				,       **a__						)
				elif model_args.model_name_or_path:
								_lowerCamelCase	:     List[Any]       =       ViTMAEConfig.from_pretrained(model_args.model_name_or_path				,       **a__						)
				else:
								_lowerCamelCase	:     int       =       ViTMAEConfig()
								logger.warning('You are instantiating a new config instance from scratch.'						)
								if model_args.config_overrides is not None:
												logger.info(f'''Overriding config: {model_args.config_overrides}'''						)
												config.update_from_string(model_args.config_overrides						)
												logger.info(f'''New config: {config}'''						)

    # adapt config
				config.update(
				    {
				        'mask_ratio': model_args.mask_ratio,
				        'norm_pix_loss': model_args.norm_pix_loss,
				    }						)

				# create image processor
				if model_args.image_processor_name:
								_lowerCamelCase	:     Union[str, Any]       =       ViTImageProcessor.from_pretrained(model_args.image_processor_name				,       **a__						)
				elif model_args.model_name_or_path:
								_lowerCamelCase	:     int       =       ViTImageProcessor.from_pretrained(model_args.model_name_or_path				,       **a__						)
				else:
								_lowerCamelCase	:     Dict       =       ViTImageProcessor()

				# create model
				if model_args.model_name_or_path:
								_lowerCamelCase	:     List[Any]       =       ViTMAEForPreTraining.from_pretrained(
								    model_args.model_name_or_path				,       from_tf=bool('.ckpt' in model_args.model_name_or_path						)				,       config=a__				,       cache_dir=model_args.cache_dir				,       revision=model_args.model_revision				,       use_auth_token=True if model_args.use_auth_token else None				,       )
				else:
								logger.info('Training new model from scratch'						)
								_lowerCamelCase	:     Tuple       =       ViTMAEForPreTraining(a__						)

				if training_args.do_train:
								_lowerCamelCase	:     Any       =       ds['train'].column_names
				else:
								_lowerCamelCase	:     Tuple       =       ds['validation'].column_names

				if data_args.image_column_name is not None:
								_lowerCamelCase	:     List[Any]       =       data_args.image_column_name
				elif "image" in column_names:
								_lowerCamelCase	:     Tuple       =       'image'
				elif "img" in column_names:
								_lowerCamelCase	:     Dict       =       'img'
				else:
								_lowerCamelCase	:     List[Any]       =       column_names[0]

				# transformations as done in original MAE paper
				# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
				if "shortest_edge" in image_processor.size:
								_lowerCamelCase	:     List[Any]       =       image_processor.size['shortest_edge']
				else:
								_lowerCamelCase	:     Dict       =       (image_processor.size['height'], image_processor.size['width'])
				_lowerCamelCase	:     Union[str, Any]       =       Compose(
				    [
				        Lambda(lambda lowercase__						: img.convert('RGB'						) if img.mode != "RGB" else img						),
				        RandomResizedCrop(a__				,       scale=(0.2, 1.0)				,       interpolation=InterpolationMode.BICUBIC						),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean				,       std=image_processor.image_std						),
				    ]						)

				def preprocess_images(lowercase__						):
								_lowerCamelCase	:     Union[str, Any]       =       [transforms(a__						) for image in examples[image_column_name]]
								return examples

				if training_args.do_train:
								if "train" not in ds:
												raise ValueError('--do_train requires a train dataset'						)
								if data_args.max_train_samples is not None:
												_lowerCamelCase	:     int       =       ds['train'].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
								# Set the training transforms
								ds["train"].set_transform(a__						)

				if training_args.do_eval:
								if "validation" not in ds:
												raise ValueError('--do_eval requires a validation dataset'						)
								if data_args.max_eval_samples is not None:
												_lowerCamelCase	:     Any       =       (
												    ds['validation'].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
												)
								# Set the validation transforms
								ds["validation"].set_transform(a__						)

				# Compute absolute learning rate
				_lowerCamelCase	:     List[Any]       =       (
				    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
				)
				if training_args.base_learning_rate is not None:
								_lowerCamelCase	:     int       =       training_args.base_learning_rate * total_train_batch_size / 256

				# Initialize our trainer
				_lowerCamelCase	:     str       =       Trainer(
				    model=a__				,       args=a__				,       train_dataset=ds['train'] if training_args.do_train else None				,       eval_dataset=ds['validation'] if training_args.do_eval else None				,       tokenizer=a__				,       data_collator=a__				,       )

				# Training
				if training_args.do_train:
								_lowerCamelCase	:     Any       =       None
								if training_args.resume_from_checkpoint is not None:
												_lowerCamelCase	:     Union[str, Any]       =       training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												_lowerCamelCase	:     Optional[Any]       =       last_checkpoint
								_lowerCamelCase	:     Union[str, Any]       =       trainer.train(resume_from_checkpoint=a__						)
								trainer.save_model()
								trainer.log_metrics('train'				,       train_result.metrics						)
								trainer.save_metrics('train'				,       train_result.metrics						)
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								_lowerCamelCase	:     List[Any]       =       trainer.evaluate()
								trainer.log_metrics('eval'				,       a__						)
								trainer.save_metrics('eval'				,       a__						)

				# Write model card and (optionally) push to hub
				_lowerCamelCase	:     Tuple       =       {
				    'tasks': 'masked-auto-encoding',
				    'dataset': data_args.dataset_name,
				    'tags': ['masked-auto-encoding'],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**a__						)
				else:
								trainer.create_model_card(**a__						)







def 	_snake_case     (							lowercase__						):
				main()


if __name__ == "__main__":
			main()







"""simple docstring"""


import math


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						if 0 not in (x, y):
												# We use the relation x^y = y*log10(x), where 10 is the base.
												return y * math.logaa(a__ )
						else:
												if x == 0:  # 0 raised to any number is 0
																		return 0
												elif y == 0:
																		return 1  # any number raised to 0 is 1
						raise AssertionError('This should never happen' )


if __name__ == "__main__":  # Main function
		# Read two numbers from input and typecast them to int using map function.
		# Here x is the base and y is the power.
		__SCREAMING_SNAKE_CASE   =   'Enter the base and the power separated by a comma: '
		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   map(int, input(prompt).split(','))
		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   map(int, input(prompt).split(','))

		# We find the log of each number, using the function res(), which takes two
		# arguments.
		__SCREAMING_SNAKE_CASE   =   res(xa, ya)
		__SCREAMING_SNAKE_CASE   =   res(xa, ya)

		# We check for the largest number
		if resa > resa:
				print('Largest number is', xa, '^', ya)
		elif resa > resa:
				print('Largest number is', xa, '^', ya)
		else:
				print('Both are equal')